Package org.encog.mathutil.randomize

Examples of org.encog.mathutil.randomize.RangeRandomizer

595
596
597
598
599
600
601
602
603
604
   */
  @Override
  public final void reset() {

    if (getLayerCount() < 3) {
      (new RangeRandomizer(-1, 1)).randomize(this);
    } else {
      (new NguyenWidrowRandomizer(-1, 1)).randomize(this);
    }
  }
View Full Code Here
440
441
442
443
444
445
446
447
448
449
450
      final boolean usePercent, final double percent) {

    final Randomizer d;

    if (useRange) {
      d = new RangeRandomizer(low, high);
    } else {
      d = new Distort(percent);
    }

    // check for errors
View Full Code Here
80
81
82
83
84
85
86
87
88
    network.addLayer(inputLayer);
    network.addLayer(outputLayer);
    network.getStructure().finalizeStructure();

    (new RangeRandomizer(-0.5, 0.5)).randomize(network);

    return network;
  }
View Full Code Here
71
72
73
74
75
76
77
78
79
80
81
    return total / SAMPLE_SIZE;
  }

  public static void main(final String args[]) {

    RangeRandomizer rangeRandom = new RangeRandomizer(-1, 1);
    NguyenWidrowRandomizer nwrRandom = new NguyenWidrowRandomizer(-1, 1);
    FanInRandomizer fanRandom = new FanInRandomizer();
    GaussianRandomizer gaussianRandom = new GaussianRandomizer(0, 1);

    System.out.println("Error improvement, higher is better.");
View Full Code Here
36
37
38
39
40
41
42
43
44
45
46
  public void testTracks()
  {   
    BasicNetwork network = EncogUtility.simpleFeedForward(5,10,15,20, true);
    double[] weights = network.getStructure().getFlat().getWeights();
    EngineArray.fill(weights, 100);
    (new RangeRandomizer(-1,1)).randomize(network);
   
    for(int i=0;i<weights.length;i++ )
    {
      Assert.assertTrue(weights[i]<10);
    }
View Full Code Here
113
114
115
116
117
118
119
120
  public void testGenetic() throws Throwable
  {
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);   
    BasicNetwork network = NetworkUtil.createXORNetworkUntrained();
    CalculateScore score = new TrainingSetScore(trainingData);
    NeuralGeneticAlgorithm genetic = new NeuralGeneticAlgorithm(network, new RangeRandomizer(-1,1), score, 500,0.1,0.25);
    NetworkUtil.testTraining(genetic,0.00001);
  }
View Full Code Here
337
338
339
340
341
342
343
344
345
346
347
      final double percentToMate = dialog.getPercentToMate().getValue();

      CalculateScore score = new TrainingSetScore(trainingData);
      final NeuralGeneticAlgorithm train = new NeuralGeneticAlgorithm(
          (BasicNetwork) file.getObject(),
          new RangeRandomizer(-1, 1), score, populationSize,
          mutationPercent, percentToMate);
      train.setTraining(trainingData);
      startup(file, train, dialog.getMaxError().getValue() / 100.0);
    }
View Full Code Here
243
244
245
246
247
248
249
250
251
252
253
  private void optionRandomize(RandomizeNetworkDialog dialog) {
    Randomizer r = null;

    switch (dialog.getType().getSelectedIndex()) {
    case 0: // Random
      r = new RangeRandomizer(dialog.getLow().getValue(), dialog
          .getHigh().getValue());
      break;
    case 1: // Nguyen-Widrow
      r = new NguyenWidrowRandomizer(dialog.getLow().getValue(), dialog
          .getHigh().getValue());
View Full Code Here
71
72
73
74
75
76
77
78
79
80
        MLTrainFactory.PROPERTY_MUTATION, false, 0.1);
    final double mate = holder.getDouble(MLTrainFactory.PROPERTY_MATE,
        false, 0.25);

    final MLTrain train = new NeuralGeneticAlgorithm((BasicNetwork) method,
        new RangeRandomizer(-1, 1), score, populationSize, mutation,
        mate);

    return train;
  }
View Full Code Here
440
441
442
443
444
445
446
447
448
449
450
      final boolean usePercent, final double percent) {

    final Randomizer d;

    if (useRange) {
      d = new RangeRandomizer(low, high);
    } else {
      d = new Distort(percent);
    }

    // check for errors
View Full Code Here
TOP

Related Classes of org.encog.mathutil.randomize.RangeRandomizer

  • org.encog.examples.neural.benchmark.WeightInitialization
  • org.encog.ml.factory.train.GeneticFactory
  • org.encog.neural.networks.BasicNetwork
  • org.encog.neural.networks.TestWeightAccess
  • org.encog.neural.networks.training.TestTraining
  • org.encog.neural.pattern.ADALINEPattern
  • org.encog.neural.prune.PruneSelective
  • org.encog.neural.rbf.RBFNetwork
  • org.encog.workbench.process.TrainBasicNetwork
  • org.encog.workbench.tabs.mlmethod.MLMethodTab
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.